Ultrasonic characterization of crack-like defects using scattering matrix similarity metrics

Ultrasonic characterization of small defects based on Res-ViT and unsupervised domain adaptation

This paper investigated the application of deep neural networks and domain adaptation for ultrasonic characterization of elliptical defects that are small and inclined. Based on performance evaluation of deep residual network (ResNet) and vision transformer (ViT), we proposed a novel Res-ViT architecture which fuses deep representative features of both models. Furthermore, we developed an unsupervised domain adaptation method to minimize the distance between the source and target domains, which is measured by maximum mean discrepancy. This approach serves to improve the generalizability of the proposed Res-ViT model in noisy environments. Simulation studies were performed at various noise levels to evaluate robustness of different deep neural networks. The proposed Res-ViT model was shown to reduce the characterization uncertainty of various defect parameters, including size, angle, and aspect ratio. Experiments were performed on three elliptical defects which have large orientation angles of 60∘ relative to the array direction. The proposed method achieved a 61% reduction in the root-mean-square error (RMSE) of defect size compared to a benchmark approach, which is based on principal component analysis and the nearest neighbor method.

Ultrasonic array imaging of porosity defects with contrast enhancement based on dominant response subtraction

Porosity defects can be found in many engineering structures and their inspection remains a challenge in the field of ultrasonic non-destructive testing. In this paper, ultrasonic array imaging of porosity defects has been studied with the aim of improving the image quality in the "dead zone", which is caused by the masking effects of the uppermost pores. The proposed approach first extracts contributions of the uppermost pores based on a single scattering model by adopting convolutional sparse coding. The extracted dominant contributions are then subtracted from the array data before forming an image, facilitating detection and localization of pores in the shadow zone. The performance of the proposed approach has been studied in simulation and experiments, and the mean localization errors of the pores are small (i.e., within 0.27 mm or 0.14λ). In addition, the effects of measurement noise and imaging parameters on robustness of the imaging result have been analyzed, providing guidelines for practical implementation of the proposed approach.

Exploring 3D elastic-wave scattering at interfaces using high-resolution phased-array system

The elastic-wave scattering at interfaces, such as cracks, is essential for nondestructive inspections, and hence, understanding the phenomenon is crucial. However, the elastic-wave scattering at cracks is very complex in three dimensions since microscopic asperities of crack faces can be multiple scattering sources. We propose a method for exploring 3D elastic-wave scattering based on our previously developed high-resolution 3D phased-array system, the piezoelectric and laser ultrasonic system (PLUS). We describe the principle of PLUS, which combines a piezoelectric transmitter and a 2D mechanical scan of a laser Doppler vibrometer, enabling us to resolve a crack into a collection of scattring sources. Subsequently, we show how the 3D elastic-wave scattering in the vicinity of each response can be extracted. Here, we experimentally applied PLUS to a fatigue-crack specimen. We found that diverse 3D elastic-wave scattering occurred in a manner depending on the responses within the fatigue crack. This is significant because access to such information will be useful for optimizing inspection conditions, designing ultrasonic measurement systems, and characterizing cracks. More importantly, the described methodology is very general and can be applied to not only metals but also other materials such as composites, concrete, and rocks, leading to progress in many fields.

Dimensionality reduction of ultrasonic array data for characterization of inclined defects based on supervised locality preserving projection

Ultrasonic arrays are increasingly used for inspection of the structural components in non-destructive testing (NDT) applications. The ultrasonic array data can be processed to form high-resolution images for detection and localization of defects. Alternatively, the scattering matrix can be extracted from the full matrix of array data and used for defect characterization if the defect size is small (i.e., comparable to an ultrasonic wavelength). This paper studies the dimensionality reduction problem of scattering matrix databases. In particular, we focus on accurate characterization of inclined defects for which previous approaches based on principal component analysis (PCA) yielded high characterization uncertainty. We propose a supervised approach based on locality preserving projection (LPP) and introduce noise constraints to the objective function of LPP. In simulation, the proposed approach is shown to produce a well-resolved defect manifold for 45°ellipses. Characterization results obtained using the simulated noisy measurements of four 60°ellipses confirm the performance improvement of LPP over PCA. In experiments, three 60°ellipses and two surface-breaking cracks have been characterized. On average, the root-mean-square (RMS) sizing error given by the LPP approach is 39.0% lower compared to PCA for the ellipses and 11.1% lower for the surface-breaking cracks.

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Accurate defect characterization is desirable in the ultrasonic nondestructive evaluation as it can provide quantitative information about the defect type and geometry. For defect characterization using ultrasonic arrays, high-resolution images can provide the size and type information if a defect is relatively large. However, the performance of image-based characterization becomes poor for small defects that are comparable to the wavelength. An alternative approach is to extract the far-field scattering coefficient matrix from the array data and use it for characterization. Defect characterization can be performed based on a scattering matrix database that consists of the scattering matrices of idealized defects with varying parameters. In this article, the problem of characterizing small surface-breaking notches is studied using two different approaches. The first approach is based on the introduction of a general coherent noise model, and it performs characterization within the Bayesian framework. The second approach relies on a supervised machine learning (ML) schema based on a scattering matrix database, which is used as the training set to fit the ML model exploited for the characterization task. It is shown that convolutional neural networks (CNNs) can achieve the best characterization accuracy among the considered ML approaches, and they give similar characterization uncertainty to that of the Bayesian approach if a notch is favorably oriented. The performance of both approaches varied for unfavorably oriented notches, and the ML approach tends to give results with higher variance and lower biases.

Optimal Extraction of Ultrasonic Scattering Features in Coarse Grained Materials

Ultrasonic array imaging is used in nondestructive testing for the detection and characterization of defects. The scattering behavior of any feature can be described by a matrix of scattering coefficients, called the scattering matrix. This information is used for characterization, and contrary to image-based analysis, the scattering matrix allows the characterization of defects at the subwavelength scale. However, the defect scattering coefficients are, in practice, contaminated by other nearby scatterers or significant structural noise. In this context, an optimal procedure to extract scattering features from a selected region of interest in a beamformed image is here investigated. This work proposes two main strategies to isolate a target scatterer in order to recover exclusively the time responses of the desired scatterer. In this article, such strategies are implemented in delay-and-sum and frequency-wavenumber forms and optimized to maximize the extraction rate. An experimental case in a polycrystalline material shows that the suggested procedures provide a rich frequency spectrum of the scattering matrix and are readily suited to minimize the effects of surrounding scattering noise. In doing so, the ability to deploy imaging methods that rely on the scattering matrix is enabled.

Deep Learning for Ultrasonic Crack Characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing.

J, Petrunin I, Raposo J, Tsourdos A, Gray I. Improving Depth Resolution of Ultrasonic Phased Array Imaging to Inspect Aerospace Composite Structures

In this paper, we present challenges and achievements in development and use of a compact ultrasonic Phased Array (PA) module with signal processing and imaging technology for autonomous non-destructive evaluation of composite aerospace structures. We analyse two different sets of ultrasonic scan data, acquired from 5 MHz and 10 MHz PA transducers. Although higher frequency transducers promise higher axial (depth) resolution in PA imaging, we face several signal processing challenges to detect defects in composite specimens at 10 MHz. One of the challenges is the presence of multiple echoes at the boundary of the composite layers called structural noise. Here, we propose a wavelet transform-based algorithm that is able to detect and characterize defects (depth, size, and shape in 3D plots). This algorithm uses a smart thresholding technique based on the extracted statistical mean and standard deviation of the structural noise. Finally, we use the proposed algorithm to detect and characterize defects in a standard calibration specimen and validate the results by comparing to the designed depth information.

Detection, Localisation and Assessment of Defects in Pipes Using Guided Wave Techniques: A Review

This paper aims to provide an overview of the experimental and simulation works focused on the detection, localisation and assessment of various defects in pipes by applying fast-screening guided ultrasonic wave techniques that have been used in the oil and gas industries over the past 20 years. Major emphasis is placed on limitations, capabilities, defect detection in coated buried pipes under pressure and corrosion monitoring using different commercial guided wave (GW) systems, approaches to simulation techniques such as the finite element method (FEM), wave mode selection, excitation and collection, GW attenuation, signal processing and different types of GW transducers. The effects of defect parameters on reflection coefficients are also discussed in terms of different simulation studies and experimental verifications.

Strategies for data acquisition using ultrasonic phased arrays

Ultrasonic phased arrays have produced major benefits in a range of fields, from medical imaging to non-destructive evaluation. The maximum information, which can be measured by an array, corresponds to the Full Matrix Capture (FMC) data acquisition technique and contains all possible combinations of transmitter–receiver signals. However, this method is not fast enough for some applications and can result in a very large volume of data. In this paper, the problem of optimal array data acquisition strategy is considered, that is, how to make the minimum number of array measurements without loss of information. The main result is that under the single scattering assumption the FMC dataset has a specific sparse structure, and this property can be used to design an optimal data acquisition method. An analytical relationship between the minimum number of array firings, maximum steering angle and signal-to-noise ratio is derived, and validated experimentally. An important conclusion is that the optimal number of emissions decreases when the angular aperture of the array increases. It is also shown that plane wave imaging data are equivalent to the FMC dataset, but requires up to an order of magnitude fewer array firings.

Eliminating backwall effects in the phased array imaging of near backwall defects

Ultrasonic array imaging is widely used to provide high quality defect detection and characterization. However, the current imaging techniques are poor at detecting and characterizing defects near a surface facing the array, as the signal scattered from the defect and the strong reflection from the planar backwall will overlap in both time and frequency domains, masking the presence of the defect. To address this problem, this paper explores imaging algorithms and relevant methods to eliminate the strong artefacts caused by the backwall reflection. The half-skip total focusing method (HSTFM), the factorization method (FM) and the time domain sampling method (TDSM) are chosen as the imaging algorithms used in this paper. Then, three methods, referred to as full matrix capture (FMC) subtraction, weighting function filtering, and the truncation method, are developed to eliminate or filter the effects caused by the strong backwall reflection. These methods can be applied easily with few tuning parameters or little prior knowledge. The performances of the proposed imaging techniques are validated in both simulation and experiments, and the results show the effectiveness of the developed methods to eliminate the artefacts caused by the backwall reflections when imaging near backwall defects.

Sizing of flaws using ultrasonic bulk wave testing: A review

Ultrasonic testing is a non-destructive method that can be used to detect, locate and size flaws. The purpose of this paper is to review techniques that utilise ultrasonic bulk waves to size flaws. Flaws that are embedded within a component (i.e. remote from any surface) as well as flaws growing from inaccessible surfaces are considered. The different available techniques are grouped into the following categories: amplitude, temporal, imaging and inversion. The principles, applications and limitations of the different techniques are covered, as well as approaches to assessing the performance of the techniques. Finally, remaining gaps and challenges in sizing flaws, particularly in an industrial setting, are discussed.

Single-Input and Multiple-Output Surface Acoustic Wave Sensing for Damage Quantification in Piezoelectric Sensors

The main aim of the paper is damage detection at the microscale in the anisotropic piezoelectric sensors using surface acoustic waves (SAWs). A novel technique based on the single input and multiple output of Rayleigh waves is proposed to detect the microscale cracks/flaws in the sensor. A convex-shaped interdigital transducer is fabricated for excitation of divergent SAWs in the sensor. An angularly shaped interdigital transducer (IDT) is fabricated at 0 degrees and ±20 degrees for sensing the convex shape evolution of SAWs. A precalibrated damage was introduced in the piezoelectric sensor material using a micro-indenter in the direction perpendicular to the pointing direction of the SAW. Damage detection algorithms based on empirical mode decomposition (EMD) and principal component analysis (PCA) are implemented to quantify the evolution of damage in piezoelectric sensor material. The evolution of the damage was quantified using a proposed condition indicator (CI) based on normalized Euclidean norm of the change in principal angles, corresponding to pristine and damaged states. The CI indicator provides a robust and accurate metric for detection and quantification of damage.

Ultrasonic defect characterisation-Use of amplitude, phase, and frequency information

This paper studies ultrasonic defect characterisation with the aim of reducing the characterisation uncertainty. Ultrasonic array data contain a mixture of responses from all reflecting features, and the scattering matrix for each defect can be extracted in post-processing, which describes how ultrasonic waves at a given incident angle are scattered by a defect. In this paper, it is shown that defect characterisation performance can be improved by the inclusion of phase and frequency information relative to current single-frequency-amplitude approaches. This superior characterisation performance is due to the increased number of informative principal components (PCs) and higher signal-to-noise ratios in the PC directions. Scattering matrix phase measurement is very sensitive to localisation errors, and an effective approach is proposed, which can be used to reliably extract phase from experimental data. Nine elliptical defects having different aspect ratios and orientation angles are characterised experimentally. The complex multi-frequency defect database has achieved up to 90.60% reduction in the quantified sizing uncertainty compared to the results obtained using only the amplitude at a single frequency.

Ultrasonic defect characterization using parametric-manifold mapping

The aim of ultrasonic non-destructive evaluation includes the detection and characterization of defects, and an understanding of the nature of defects is essential for the assessment of structural integrity in safety critical systems. In general, the defect characterization challenge involves an estimation of defect parameters from measured data. In this paper, we explore the extent to which defects can be characterized by their ultrasonic scattering behaviour. Given a number of ultrasonic measurements, we show that characterization information can be extracted by projecting the measurement onto a parametric manifold in principal component space. We show that this manifold represents the entirety of the characterization information available from far-field harmonic ultrasound. We seek to understand the nature of this information and hence provide definitive statements on the defect characterization performance that is, in principle, extractable from typical measurement scenarios. In experiments, the characterization problem of surface-breaking cracks and the more general problem of elliptical voids are studied, and a good agreement is achieved between the actual parameter values and the characterization results. The nature of the parametric manifold enables us to explain and quantify why some defects are relatively easy to characterize, whereas others are inherently challenging.

The detection of flaws in austenitic welds using the decomposition of the time-reversal operator

The non-destructive testing of austenitic welds using ultrasound plays an important role in the assessment of the structural integrity of safety critical structures. The internal microstructure of these welds is highly scattering and can lead to the obscuration of defects when investigated by traditional imaging algorithms. This paper proposes an alternative objective method for the detection of flaws embedded in austenitic welds based on the singular value decomposition of the time-frequency domain response matrices. The distribution of the singular values is examined in the cases where a flaw exists and where there is no flaw present. A lower threshold on the singular values, specific to austenitic welds, is derived which, when exceeded, indicates the presence of a flaw. The detection criterion is successfully implemented on both synthetic and experimental data. The datasets arising from welds containing a flaw are further interrogated using the decomposition of the time-reversal operator (DORT) method and the total focusing method (TFM), and it is shown that images constructed via the DORT algorithm typically exhibit a higher signal-to-noise ratio than those constructed by the TFM algorithm.

Characterization of defects using ultrasonic arrays: a dynamic classifier approach

In the field of nondestructive evaluation, accurate characterization of defects is required for the assessment of defect severity. Defect characterization is studied in this paper through the use of the ultrasonic scattering matrix, which can be extracted from the array measurements. Defects that have different shapes are classified into different defect classes, and this essentially allows us to distinguish between crack-like defects and volumetric voids. Principal component analysis (PCA) is used for feature extraction, and several representational principal component subsets are found through exhaustive searching in which quadratic discriminant analysis (QDA) and support vector machine (SVM) are used as the pattern classifiers. Instead of choosing a single optimal classifier, the best classifier is dynamically selected for different measurements by estimating the local classifier accuracy. The proposed approach is validated in simulation and experiments. In simulation, the depths (lengths of the minor axes) of 4441 out of 4636 test samples are measured accurately, and the measurement errors (with respect to the defect size) are below 10%. Arbitrarily shaped rough volumetric defects are identified as ellipses, which are reasonably good matches in shape to the original defects. Experimentally, six subwavelength scatterers are characterized and sized to within 0.14λ.